Protein-ligand binding affinity prediction using multi-instance learning with docking structures
IntroductionRecent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. These methods complement physics-based computational modeling such as molecular docking for virtual high-throughput screening. Des...
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| Format: | Article |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2024.1518875/full |
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| author | Hyojin Kim Heesung Shim Aditya Ranganath Stewart He Garrett Stevenson Jonathan E. Allen |
| author_facet | Hyojin Kim Heesung Shim Aditya Ranganath Stewart He Garrett Stevenson Jonathan E. Allen |
| author_sort | Hyojin Kim |
| collection | DOAJ |
| description | IntroductionRecent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. These methods complement physics-based computational modeling such as molecular docking for virtual high-throughput screening. Despite recent advances and improved predictive performance, most methods in this category primarily rely on utilizing co-crystal complex structures and experimentally measured binding affinities as both input and output data for model training. Nevertheless, co-crystal complex structures are not readily available and the inaccurate predicted structures from molecular docking can degrade the accuracy of the machine learning methods.MethodsWe introduce a novel structure-based inference method utilizing multiple molecular docking poses for each complex entity. Our proposed method employs multi-instance learning with an attention network to predict binding affinity from a collection of docking poses.ResultsWe validate our method using multiple datasets, including PDBbind and compounds targeting the main protease of SARS-CoV-2. The results demonstrate that our method leveraging docking poses is competitive with other state-of-the-art inference models that depend on co-crystal structures.DiscussionThis method offers binding affinity prediction without requiring co-crystal structures, thereby increasing its applicability to protein targets lacking such data. |
| format | Article |
| id | doaj-art-8b6ab930ce45489980464c1ea6a967ed |
| institution | DOAJ |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-8b6ab930ce45489980464c1ea6a967ed2025-08-20T02:43:12ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-01-011510.3389/fphar.2024.15188751518875Protein-ligand binding affinity prediction using multi-instance learning with docking structuresHyojin Kim0Heesung Shim1Aditya Ranganath2Stewart He3Garrett Stevenson4Jonathan E. Allen5Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, United StatesBiosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, United StatesCenter for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, United StatesGlobal Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA, United StatesComputational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA, United StatesGlobal Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA, United StatesIntroductionRecent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. These methods complement physics-based computational modeling such as molecular docking for virtual high-throughput screening. Despite recent advances and improved predictive performance, most methods in this category primarily rely on utilizing co-crystal complex structures and experimentally measured binding affinities as both input and output data for model training. Nevertheless, co-crystal complex structures are not readily available and the inaccurate predicted structures from molecular docking can degrade the accuracy of the machine learning methods.MethodsWe introduce a novel structure-based inference method utilizing multiple molecular docking poses for each complex entity. Our proposed method employs multi-instance learning with an attention network to predict binding affinity from a collection of docking poses.ResultsWe validate our method using multiple datasets, including PDBbind and compounds targeting the main protease of SARS-CoV-2. The results demonstrate that our method leveraging docking poses is competitive with other state-of-the-art inference models that depend on co-crystal structures.DiscussionThis method offers binding affinity prediction without requiring co-crystal structures, thereby increasing its applicability to protein targets lacking such data.https://www.frontiersin.org/articles/10.3389/fphar.2024.1518875/fullAI-driven drug developmentvirtual high-throughput screeningprotein-ligand interactionmolecular docking3D atomic graph representationstructure-based machine learning |
| spellingShingle | Hyojin Kim Heesung Shim Aditya Ranganath Stewart He Garrett Stevenson Jonathan E. Allen Protein-ligand binding affinity prediction using multi-instance learning with docking structures Frontiers in Pharmacology AI-driven drug development virtual high-throughput screening protein-ligand interaction molecular docking 3D atomic graph representation structure-based machine learning |
| title | Protein-ligand binding affinity prediction using multi-instance learning with docking structures |
| title_full | Protein-ligand binding affinity prediction using multi-instance learning with docking structures |
| title_fullStr | Protein-ligand binding affinity prediction using multi-instance learning with docking structures |
| title_full_unstemmed | Protein-ligand binding affinity prediction using multi-instance learning with docking structures |
| title_short | Protein-ligand binding affinity prediction using multi-instance learning with docking structures |
| title_sort | protein ligand binding affinity prediction using multi instance learning with docking structures |
| topic | AI-driven drug development virtual high-throughput screening protein-ligand interaction molecular docking 3D atomic graph representation structure-based machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2024.1518875/full |
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